CN115147159B - Optical storage and charging integrated bus charging station site selection method - Google Patents
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Abstract
The invention discloses an optical storage and charging integrated bus charging station site selection method, which comprises the following steps: constructing a random photovoltaic power generation output power scene set based on historical weather data and solar irradiance data; determining the bus hourly departure frequency based on bus operation data; constructing an optical storage and charging integrated public transport charging station site selection optimization model; solving an optical storage and charge integrated bus charging station site selection optimization model by adopting an L-type decomposition algorithm; and checking whether the L-type decomposition algorithm meets the termination condition. The present invention aims to minimize the weighted sum of the charging facility construction cost, the bus charging cost, and the carbon footprint cost. Based on the address selection method, certain social and economic benefits can be obtained for public transport operators.
Description
Technical Field
The invention relates to the technical field of public transportation planning and management, in particular to an optical storage and charging integrated public transportation charging station site selection method.
Background
The light storage and charging integrated bus charging station refers to a bus charging station depending on a light storage and charging integrated charging facility. The light stores up and fills integration facility of charging includes photovoltaic power generation system, energy storage system, fills electric pile and intelligent little electric wire netting management system. Solar cell modules are typically mounted on building roofs, parking shed roofs, garage tops, and the like. The energy storage system is used for storing electric energy produced by the solar panel. The charging pile is a three-port photovoltaic grid-connected charging pile, and double electric energy supply of the local photovoltaic power generation system and the public power distribution network to the vehicle can be realized. The intelligent micro-grid management system has the functions of monitoring, diagnosing and managing the optical storage and charging integrated charging facilities.
Under the general condition, the light storage and charging integrated charging facility has the following advantages: (1) Part of electric energy is generated on the demand side in a green low-carbon mode through a solar panel, so that dependence on a power grid is reduced; (2) The battery and the energy storage system of the electric automobile can jointly relieve the negative influence of large-scale photovoltaic grid connection on the power distribution network; (3) The charging cost of the public transport operator can be reduced through the dynamic response to the time-of-use electricity price. At present, a bus charging station based on an optical storage and charging integrated charging facility is applied to the ground, and remarkable social and economic benefits are obtained.
However, the light storage and charging integrated charging facility is rarely applied to the field of buses in a floor mode. One of the main reasons is the lack of a light storage and charging integrated public transportation charging station site selection method capable of quantitatively and comprehensively planning social benefits and economic benefits. Therefore, how to provide a scientific and practical method for selecting addresses of light storage and charging integrated bus charging stations is a problem that needs to be solved by those skilled in the art.
Disclosure of Invention
In view of the above, the invention provides an optical storage and charging integrated bus charging station site selection method which fully considers the uncertainty of photovoltaic power generation output power. Under the conditions of historical weather data, solar irradiance data and bus operation data, the problem of site selection of the optical storage and charging integrated bus charging sites in a large-scale random photovoltaic power generation output power scene is processed by means of an L-type decomposition algorithm, so that an optimal optical storage and charging integrated bus charging site selection scheme is automatically generated.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
an optical storage and charging integrated bus charging station address selection method comprises the following steps:
Constructing a photovoltaic power generation output power random scene set based on historical weather data and solar irradiance data;
Determining the bus hourly departure frequency based on bus operation data;
constructing an optical storage and charging integrated bus charging station site selection optimization model based on the bus departure frequency per hour and the photovoltaic power generation output power random scene set;
And solving an optical storage and charging integrated bus charging station site selection optimization model by adopting an L-type decomposition algorithm, and terminating the L-type decomposition algorithm under the condition of meeting a termination condition.
Preferably, constructing the photovoltaic power generation output power random scene set specifically includes:
calculating historical photovoltaic output power based on the historical weather data and solar irradiance data;
and constructing a photovoltaic power generation output power random scene set based on the historical photovoltaic output power set.
Preferably, the historical photovoltaic output power calculation formula is:
Wherein, T cell represents the solar cell temperature, T NOCT represents the nominal solar cell working temperature, ρ represents the power temperature coefficient, P r represents the rated power of the photovoltaic power generation module, ψ n represents the solar irradiance, and T a represents the air temperature.
Preferably, the frequency of bus departure per hour is determined by the GPS data of the bus at the starting station and the card swiping data of the passengers.
Preferably, the optimal model for site selection of the light storage and charging integrated bus charging station is as follows:
In the objective function, w represents a bus queue index, j represents a candidate bus charging station index, and alpha j1 represents the cost of building the optical storage and charging integrated charging station at j; the cost of constructing a common charging station at j by alpha j2; Representing the number of days in a year; alpha 3 represents the bus hour running cost; alpha 4 represents a bus purchase unit price; x j1 represents a 0-1 variable, if the candidate charging station j establishes an optical storage and charging integrated charging station, the value is 1, otherwise, the value is 0; x j2 represents a 0-1 variable, if the candidate charging station j establishes a common charging station, the value is 1, otherwise, the value is 0; y ii',j,t represents the bus flow to the charging station j on the bus line ii' at the t-th hour; t i'j and t ji' represent travel times of the buses from i 'to j and j to i', respectively; n w represents the number of vehicles in the bus fleet w; EQ (x 1, g, p) represents the expectation of the sum of the costs of charging and carbon emissions of the vehicle.
Preferably, the method for solving the optical storage and charging integrated bus charging station site selection optimization model by adopting the L-type decomposition algorithm specifically comprises the following steps:
Decomposing an optical storage and filling integrated public transportation charging station site selection optimization model into a main problem and a plurality of sub-problems;
And adopting an L-type decomposition algorithm to sequentially and iteratively solve the main problem and the plurality of sub-problems.
Preferably, the main problems are:
θ≥Cut(S)
θ represents a variable, cut (S) represents all the optimal cuts in the optimal Cut set S;
the charging station type constraints are:
j is a candidate bus charging station set;
The charging traffic flow constraint is as follows:
Wherein R represents a sufficiently large positive number;
the conservation constraint of the charging hours is as follows:
Wherein, AndIndicating the set departure times of ii 'and i' i lines in the T hour, h 'ii',t indicating the residence time of the bus flow y ii',j,t in the T hour, h' i′i,t indicating the residence time of the bus flow y i'i,j,t in the T hour, h ii',j,t indicating the total charging time of the bus flow y ii',j,t at the charging station j, h i'i,j,t indicating the total charging time of the bus flow y i'i,j,t at the charging station j, W being a fleet set, T indicating an hour index, T being an hour set;
The constraint indicates that bus flow y ii',j,t is when the total charging time of charging station j does not exceed y ii',j,t;
The constraint indicates that the total charging time of all bus flows at a charging station j in a t-th period is not more than c j & 1, wherein c j is the number of charging piles owned by the j-th bus charging station;
The constraint indicates that the total charge of the bus flow y ii',j,t is less than (1- η min)Ewyii',j,t, where p gr indicates the charging power of the charging post, η min indicates the minimum SoC allowed by the vehicle, and E w indicates the battery capacity of each vehicle in the bus fleet w;
The constraint defines the total charge of bus flow y ii',j,t, g ii',j,t represents the total charge of bus flow y ii',j,t at charging station j;
The constraint indicates that the total remaining capacity of the fleet w should be not less than η tEwNw in the t-th hour, where η t indicates the minimum SoC allowed by the vehicle per hour, e ii' and e i'i indicate the energy consumption of the buses from i to i 'and from i' to i, respectively, e ij and e ji indicate the energy consumption of the buses from i to j and from j to i, respectively, e i'j and e ji' indicate the energy consumption of the buses from i 'to j and from j to i', respectively, g i'i,j,t' indicates the charge amount of the bus flow of the line i 'to the charging station j in the t' period, and g ii',j,t' indicates the charge amount of the bus flow of the line ii 'to the charging station j in the t' period.
The constraint indicates that the total remaining capacity of fleet w should not exceed E wNw within hour t;
xj1,xj2∈{0,1},j∈J
Preferably, the sub-problems are:
Where k| is the number of elements of the set of random photovoltaic power generation scenarios K, m represents the month index, D m represents the number of days in month m, δ pv represents the carbon footprint cost per 1 degree of electricity produced using the photovoltaic power generation system, δ gr represents the carbon footprint cost per 1 degree of electricity produced by the coal-fired power plant, λ t represents the utility grid's t hour price of electricity, λ t' represents the photovoltaic power generation's t hour recovered price of electricity, a j represents the number of panels that the charging station j can accommodate, p mtk represents the photovoltaic output power at the mth month't hour in the kth random scenario, v mjtk and u mjtk represent the v mjt and u mjt variables, respectively, in the kth random photovoltaic power generation scenario;
the constraint indicates that the total electric quantity stored in the energy storage system by the photovoltaic power generation system in the charging station j in the month M and the time t is not more than the electric quantity generated by the photovoltaic system, wherein v mjtk indicates the total electric quantity stored in the energy storage system by the photovoltaic power generation system in the charging station j in the month M and the time t in the kth random scene, p mtk indicates the power generation output power of a unit photovoltaic cell panel in the month M and the t hours in the kth random scene, and M is a month set;
The constraint indicates that the total electric quantity transfer of the photovoltaic power generation from the energy storage system to the automobile battery in the charging station j in month m and time t does not exceed the total requirement of the charging of the motorcade, wherein u mjtk indicates the total electric quantity transfer of the photovoltaic power generation from the energy storage system to the automobile battery in the charging station j in month m and time t in the kth random scene;
The constraint indicates that the current total energy storage amount of the energy storage system does not exceed the energy storage capacity E' j;vmjsk, the total electric quantity stored in the energy storage system by the photovoltaic power generation system in the charging station j in the month m and the time s in the kth random scene, and u mjsk indicates the total electric quantity transfer of the photovoltaic power generation from the energy storage system to the automobile battery in the charging station j in the month m and the time s in the kth random scene.
The constraint indicates that the current total energy storage amount of the energy storage system is more than or equal to 0;
Preferably, the termination condition is set to a program run time of more than 10 hours or a number of iterations of more than 1000.
According to the technical scheme, the invention discloses an optical storage and charging integrated bus charging station site selection method. First, a random photovoltaic power generation output power scene set is constructed from known historical weather data and solar irradiance data. The historical photovoltaic output power is obtained according to a photovoltaic output power calculation formula, a plurality of historical photovoltaic output power modes are obtained by adopting a clustering method based on a historical photovoltaic output power set, and different output power modes are extracted by further adopting a random sampling technology, so that a photovoltaic power generation output power random scene set is constructed. Secondly, determining the bus per hour departure frequency based on bus operation data, wherein the departure frequency needs to be met as an important constraint condition in an optical storage and charging integrated bus charging station site selection optimization model. Next, constructing an optical storage and charging integrated public transportation charging station site selection optimization model, and adopting a mixed integer linear programming model based on bus flow dispatching for two purposes: firstly, compared with an optimization model based on vehicle dispatching, the optimization model based on bus flow dispatching is suitable for an example with a large-scale network; secondly, an optimization model based on bus flow dispatching clearly expresses key modeling elements such as fleet charging, dispatching, energy consumption and the current electric quantity of a battery. On the basis, an L-type decomposition algorithm is adopted to solve an optical storage and charge integrated bus charging station site selection optimization model.
Compared with the prior art, the method and the device have the advantages that the site selection, the traffic flow scheduling, the traffic flow charging and the energy scheduling of the charging station are jointly optimized; the random photovoltaic power generation output power scene set is used for guaranteeing that the optimal model provides a desired minimum target value; the L-type decomposition algorithm is adopted to ensure the resolvable property of the optimization model in the face of a large-scale complex network, and a technical method in the aspects of planning and management is provided for popularization of the optical storage and charging integrated charging facilities in the public transportation field.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present invention, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for selecting addresses of an optical storage and charging integrated bus charging station.
Fig. 2 is a schematic diagram of the structure and function of the light storage and charging integrated charging facility provided by the invention.
Fig. 3 is a diagram of geographic information of a public transportation network according to an embodiment of the present invention.
Fig. 4 is a diagram of an optimal site selection scheme of an optical storage and charging integrated charging station according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, the embodiment of the invention discloses a method for selecting addresses of light storage and charging integrated bus charging stations. Each candidate bus charging station can choose to not establish a charging station, establish a common charging station and establish an optical storage and charging integrated charging station. The structure and function of the light storage and charging integrated charging facility are described below with reference to fig. 2. The charging pile is connected with a public power grid and an energy storage system. Thus, a bus can receive both electrical energy generated from a photovoltaic power generation system and electrical energy from a utility grid. The electric energy generated by the photovoltaic power generation system can be directly sold on the internet or stored in the energy storage system. The intelligent micro-grid management system has the functions of monitoring, diagnosing and managing the optical storage and charging integrated charging facility, and is a central center of the local optical storage and charging integrated charging facility.
The method for selecting the address of the light storage and charge integrated bus charging station comprises the following steps:
s1, constructing a photovoltaic power generation output power random scene set based on historical weather data and solar irradiance data. When weather data and solar irradiance data are known, the photovoltaic power generation output power P n has the following calculation formula:
Wherein, T cell represents the solar cell temperature, T NOCT represents the nominal solar cell working temperature, ρ represents the power temperature coefficient, P r represents the rated power of the photovoltaic power generation module, ψ n represents the solar irradiance, and T a represents the air temperature.
Based on the historical photovoltaic output power set, a plurality of historical photovoltaic output power modes are obtained by adopting a K-means clustering method, and different output power modes are extracted by further adopting a random sampling technology, so that a photovoltaic power generation output power random scene set is constructed. A random scene is represented by a 12 by 24 matrix, the rows of the matrix represent months, the columns represent hours, the elements represent the unit photovoltaic cell panel power generation output power of the mth and the t hours, and the matrix set forms a photovoltaic power generation output power random scene set. Let p mtk represent the unit photovoltaic cell panel power generation output power of the mth month and the t hour under the kth scene.
S2, determining the hourly departure frequency of the buses based on the bus operation data. The hourly departure frequency of the buses is jointly determined by the GPS data of the buses at the starting station and the card swiping data of passengers. The bus GPS data records the ID, position, speed, acceleration, time, line and other information of each bus. The passenger card swiping data records information such as boarding time, stations, lines and the like of passengers. By matching the GPS data of the bus with the card swiping data of passengers, the departure time of each bus route can be obtained, and the departure frequency of each bus hour can be obtained.
S3, constructing an optical storage and charging integrated public transportation charging station site selection optimization model. Table 1 lists the parameters and variables of the optimization model.
The optimization model is expressed as follows:
In the objective function, M represents a month index, and M is a month set; t represents an hour index, and T is an hour set; ii' represents a bus route index, L is a route set; w represents a bus fleet index, and W is a fleet set; j represents a candidate bus charging station index, and J is a candidate bus charging station set; α j1 represents the cost (yuan/year) of building the optical storage-charging integrated charging station at j; the cost (yuan/year) of constructing a common charging station at j by alpha j2; Representing the number of days in a year; α 3 represents bus hour running cost (yuan/hour); α 4 represents bus purchase unit price (yuan/year); x j1 represents a 0-1 variable, if the candidate charging station j establishes an optical storage and charging integrated charging station, the value is 1, otherwise, the value is 0; x j2 represents a 0-1 variable, if the candidate charging station j establishes a common charging station, the value is 1, otherwise, the value is 0; y ii',j,t represents the bus flow to the charging station j on the bus line ii' at the t-th hour; t i'j and t ji' represent travel times of the buses from i 'to j and j to i', respectively; n w represents the number of vehicles in the bus fleet w; EQ (x 1, g, p) represents the expectation of the sum of the costs of charging and carbon emissions of the vehicle.
The constraint is a charging station type constraint.
The constraint is a charging traffic constraint. Wherein R represents a sufficiently large positive number.
The constraint is a hourly conservation constraint.AndIndicating the number of scheduled departure times of the ii 'and i' i lines in the t-th hour. h i'i',t represents the residence time of bus flow y ii',j,t within hour t. h' i′i,t represents the residence time of bus flow y i'i,j,t within hour t. h ii',j,t represents the total charging time of bus flow y ii',j,t at charging station j, and h i'i,j,t represents the total charging time of bus flow y i'i,j,t at charging station j.
This constraint indicates that bus flow y ii',j,t is when the total charge time of charge station j does not exceed y ii',j,t.
The constraint indicates that the total charging time of all bus flows to the charging station j in the t-th period does not exceed c j.1. Wherein c j is the number of charging piles owned by the jth bus charging station.
This constraint indicates that the total charge of the bus flow y ii',j,t is less than (1- η min)Ewyii',j,t. Where p gr indicates the charge pile charge power η min indicates the minimum SoC allowed for the vehicle (typically 20%). E w indicates the battery capacity of each vehicle in the bus fleet w.
The constraint defines the total charge of the bus flow y ii',j,t. g ii',j,t represents the total charge of bus flow y ii',j,t at charging station j.
The constraint indicates that the total remaining power of fleet w should be no less than η tEwNw within hour t. Where η t denotes the minimum SoC allowed by the vehicle per hour. e ii' and e i'i represent the energy consumption of the bus from i to i 'and from i' to i, respectively. e ij and e ji represent the energy consumption of the buses from i to j and j to i, respectively, e i'j and e ji' represent the energy consumption of the buses from i 'to j and j to i', respectively, g i'i,j,t' represents the charge amount of the bus flow of the line i 'i in the t' th period of time at the charging station j, and g ii',j,t' represents the charge amount of the bus flow of the line ii 'in the t' th period of time at the charging station j.
The constraint indicates that the total remaining power of fleet w should not exceed E wNw within hour t.
xj1,xj2∈{0,1},j∈J
The constraints define the range of values of the decision variables.
Wherein D m represents the number of days in month m. Delta pv represents the cost of the carbon footprint per 1 degree of electricity produced using the photovoltaic power generation system. Delta gr represents the cost of the carbon footprint per 1 degree electricity produced by a coal-fired power plant. Lambda t represents the electricity price of the public network at the t-th hour. Lambda t' represents the electricity price recovered at the t-th hour of photovoltaic power generation. A j represents the number of panels that can be accommodated by the charging station j. p mtk represents the photovoltaic output power at the mth month and t hours in the kth random scenario, v mjtk and u mjtk represent the v mjt and u mjt variables, respectively, in the kth random scenario of photovoltaic power generation, u mjt represents the total power transfer (kilowatt-hour) of photovoltaic power generation from the energy storage system to the car battery in charging station j during month m, time t, and v mjt represents the total power (kilowatt-hour) of photovoltaic power generation system stored in the energy storage system in charging station j during month m, time t.
The constraint indicates that the total electric quantity stored in the energy storage system by the photovoltaic power generation system in the charging station j in month m and time t does not exceed the electric quantity generated by the photovoltaic system. Wherein v mjtk represents the total electric quantity stored in the energy storage system by the photovoltaic power generation system in the charging station j in month M and time t in the kth random scene, p mtk represents the power generation output power of the unit photovoltaic cell panel in month M and time t in the kth random scene, and M is a month set;
The constraint indicates that the total power transfer of photovoltaic power generation from the energy storage system to the vehicle battery in charging station j at month m, time t, does not exceed the fleet total charge demand. Wherein u mjtk represents the total electric quantity transfer of photovoltaic power generation from the energy storage system to the automobile battery in the charging station j at month m and time t in the kth random scene.
The constraint indicates that the current total energy storage of the energy storage system does not exceed the energy storage capacity E' j.
The constraint indicates that the current total energy storage amount of the energy storage system is greater than or equal to 0.
The two constraints define the range of values of the decision variables.
The optical storage and charging integrated public transportation charging station site selection optimization model is a mixed integer linear programming model based on bus flow scheduling. The optimization model provides decisions such as site selection, traffic flow scheduling, traffic flow charging, energy scheduling and the like of the charging station. The optimization model objective function is to minimize the sum of the cost of charging, the cost of construction of the charging station, the cost of operation of the vehicle, and the cost of the carbon footprint. Constraints include traffic organization constraints, fleet current SoC constraints, site selection constraints, and technical constraints related to photovoltaic power generation and vehicle charging.
And S4, solving an optical storage and charge integrated bus charging station site selection optimization model by adopting an L-type decomposition algorithm. The specific solving steps are as follows:
the first step, enabling theta to be in an infinite state, enabling S to be an empty set, enabling S to represent a set for storing optimal cuts, and enabling theta to be an artificial variable in the main problem of the algorithm;
secondly, solving a main problem by using a commercial solver;
and a third step of: solving the sub-problem using a commercial solver;
fourth step, if Wherein θ *,G * is the optimal solution corresponding to the main problem. Terminating the algorithm; otherwise, adding the optimal cut into the main problem, updating S, and returning to the second step.
Wherein, the optimal cut is expressed as follows:
Here, |k| is the number of elements of the random photovoltaic power generation scene set K. Is a dual variable of the sub-problem. Let Cut (S) represent all the optimal cuts in the optimal Cut set S.
Wherein the main problem is expressed as follows:
θ≥Cut(S)
xj1,xj2∈{0,1},j∈J
wherein the sub-problem is represented as follows:
Wherein v mjtk and u mjtk represent v mjt and u mjt variables, respectively, in the kth photovoltaic power generation random scenario.
S5, checking whether the L-type decomposition algorithm meets a termination condition. The L-type decomposition algorithm automatically terminates when the program run time exceeds 10 hours or the number of iterations exceeds 1000. Therefore, the L-type decomposition algorithm can be terminated by meeting any one of the following conditions: (1) Program run time exceeds 10 hours or iteration times exceeds 1000 times; (2)
In the following embodiment of the invention, as shown in fig. 2, 34 (bidirectional) real bus routes of a city a are shown, and 20 bus start/end stations and 15 candidate bus charging stations are shown. The bus operation period selected in the embodiment is 5: 00-23: 00, bus maintenance and rest period is 23: 00-the next day 5:00. bus operation data, historical weather data and solar irradiance data cover all months of 2019. The specific parameters of the optimization model are known information of the present invention and will not be further described herein.
S1, constructing a random photovoltaic power generation output power scene set based on historical weather data and solar irradiance data. The photovoltaic Power generation panel used in the examples was Sun Power E20-327. The specific parameters are shown in table 1.
Table 1: sun Power E20-327 photovoltaic panel parameters
The historical photovoltaic panel output power can be calculated from the parameters of table 1. Based on the historical photovoltaic output power set, a K-means clustering method is adopted to obtain a plurality of historical photovoltaic output power modes, and the results are shown in Table 2.
Table 2: k-means clustering results
And further adopting a random sampling technology to extract different output power modes, thereby constructing a photovoltaic power generation output power random scene set. A random scene is represented by a 12 by 24 matrix, the rows of the matrix represent months, the columns represent hours, the elements represent the unit photovoltaic cell panel power generation output power of the mth and the t hours, and the matrix set forms a photovoltaic power generation output power random scene set. Let p mtk represent the unit photovoltaic cell panel power generation output power of the mth month and the t hour under the kth scene.
S2, determining the hourly departure frequency of the buses based on the bus operation data. The GPS data of the bus is matched with the card swiping data of passengers through the SQ Server, so that the departure time of each bus route can be obtained, and the departure frequency of each bus hour can be obtained.
S3, constructing an optical storage and charging integrated public transportation charging station site selection optimization model. Based on the embodiment, the parameter assignment of the model in table 1 is substituted into the optimization model, so that an example of the optimization model can be formed.
S4, solving an optical storage and charging integrated bus charging station site selection optimization model by adopting an L-type decomposition algorithm. According to an example of solving an optimization model in a specific solving step of an L-type decomposition algorithm, writing a program for executing the L-type decomposition algorithm by using MATLAB, and calling Gurobi a solver to solve a main problem and a sub-problem.
S5, checking whether the L-type decomposition algorithm meets a termination condition. Fig. 4 shows an optimal site selection scheme of an optical storage and charging integrated charging station according to an embodiment of the present invention.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (7)
1. An optical storage and charging integrated bus charging station site selection method is characterized by comprising the following steps of:
Constructing a photovoltaic power generation output power random scene set based on historical weather data and solar irradiance data;
Determining the bus hourly departure frequency based on bus operation data;
constructing an optical storage and charging integrated bus charging station site selection optimization model based on the bus departure frequency per hour and the photovoltaic power generation output power random scene set;
adopting an L-type decomposition algorithm to solve an optical storage and charging integrated bus charging station site selection optimization model, and terminating the L-type decomposition algorithm to meet a termination condition;
the optical storage and charging integrated public transportation charging station site selection optimization model is as follows:
In the objective function, w represents a bus queue index, j represents a candidate bus charging station index, and alpha j1 represents the cost of building the optical storage and charging integrated charging station at j; α j2 represents the cost of building a common charging station at j; Representing the number of days in a year; alpha 3 represents the bus hour running cost; alpha 4 represents a bus purchase unit price; x j1 represents a 0-1 variable, if the candidate charging station j establishes an optical storage and charging integrated charging station, the value is 1, otherwise, the value is 0; x j2 represents a 0-1 variable, if the candidate charging station j establishes a common charging station, the value is 1, otherwise, the value is 0; y ii',j,t represents the bus flow to the charging station j on the bus line ii' at the t-th hour; t i'j and t ji' represent travel times of the buses from i 'to j and j to i', respectively; n w represents the number of vehicles in the bus fleet w; EQ (x 1, g, p) represents the expectation of the sum of the costs of charging and carbon emissions of the vehicle;
the method for solving the optical storage and charging integrated bus charging station site selection optimization model by adopting the L-type decomposition algorithm specifically comprises the following steps:
Decomposing an optical storage and filling integrated public transportation charging station site selection optimization model into a main problem and a plurality of sub-problems;
And adopting an L-type decomposition algorithm to sequentially and iteratively solve the main problem and the plurality of sub-problems.
2. The method for selecting addresses for the light-storage-and-charge integrated bus charging station according to claim 1, wherein the construction of the photovoltaic power generation output power random scene set specifically comprises the following steps:
calculating historical photovoltaic output power based on the historical weather data and solar irradiance data;
and constructing a photovoltaic power generation output power random scene set based on the historical photovoltaic output power set.
3. The method for selecting the site of the light-storage-and-charge integrated bus charging station according to claim 2, wherein the calculation formula of the historical photovoltaic output power P n is as follows:
Wherein, T cell represents the solar cell temperature, T NOCT represents the nominal solar cell working temperature, ρ represents the power temperature coefficient, P r represents the rated power of the photovoltaic power generation module, ψ n represents the solar irradiance, and T a represents the air temperature.
4. The method for locating an optical storage and charging integrated bus charging station according to claim 1, wherein the bus departure frequency per hour is determined by bus GPS data and passenger card swiping data at the departure station.
5. The method for selecting addresses for the light storage and charge integrated bus charging station as set forth in claim 1, wherein the method is characterized in that the following problems are:
θ≥Cut(S)
θ represents a variable, cut (S) represents all the optimal cuts in the optimal Cut set S;
the charging station type constraints are:
j is a candidate bus charging station set;
The charging traffic flow constraint is as follows:
Wherein R represents a sufficiently large positive number;
the conservation constraint of charging hours is:
Wherein, AndIndicating the set departure times of ii 'and i' i lines in the T hour, h 'ii',t indicating the residence time of the bus flow y ii',j,t in the T hour, h' i'i,t indicating the residence time of the bus flow y i'i,j,t in the T hour, h ii',j,t indicating the total charging time of the bus flow y ii',j,t at the charging station j, h i'i,j,t indicating the total charging time of the bus flow y i'i,j,t at the charging station j, W being a fleet set, T indicating an hour index, T being an hour set;
The constraint indicates that bus flow y ii',j,t is when the total charging time of charging station j does not exceed y ii',j,t;
The constraint indicates that the total charging time of all bus flows at a charging station j in a t-th period is not more than c j & 1, wherein c j is the number of charging piles owned by the j-th bus charging station;
The constraint indicates that the total charge of the bus flow y ii',j,t is less than (1- η min)Ewyii',j,t, where p gr indicates the charging power of the charging post, η min indicates the minimum SoC allowed by the vehicle, and E w indicates the battery capacity of each vehicle in the bus fleet w;
The constraint defines the total charge of bus flow y ii',j,t, g ii',j,t represents the total charge of bus flow y ii',j,t at charging station j;
The constraint indicates that the total remaining capacity of the fleet w should be not less than η tEwNw in the t-th hour, wherein η t indicates the minimum SoC allowed by the vehicle per hour, e ii' and e i'i indicate the energy consumption of the buses from i to i 'and from i' to i, respectively, e ij and e ji indicate the energy consumption of the buses from i to j and from j to i, respectively, e i'j and e ji' indicate the energy consumption of the buses from i 'to j and from j to i', respectively, g i'i,j,t' indicates the charge amount of the bus flow of the line i 'to the charging station j in the t' period, g ii',j,t' indicates the charge amount of the bus flow of the line ii 'to the charging station j in the t' period;
The constraint indicates that the total remaining capacity of fleet w should not exceed E wNw within hour t;
xj1,xj2∈{0,1},j∈J
6. the method for locating an optical storage and charging integrated bus charging station according to claim 5, wherein the sub-problems are as follows:
Where k| is the number of elements of the set of random photovoltaic power generation scenarios K, m represents the month index, D m represents the number of days in month m, δ pv represents the carbon footprint cost per 1 degree of electricity produced using the photovoltaic power generation system, δ gr represents the carbon footprint cost per 1 degree of electricity produced by the coal-fired power plant, λ t represents the utility grid's t hour price of electricity, λ t' represents the photovoltaic power generation's t hour recovered price of electricity, a j represents the number of panels that the charging station j can accommodate, p mtk represents the photovoltaic output power at the mth month't hour in the kth random scenario, v mjtk and u mjtk represent the v mjt and u mjt variables, respectively, in the kth random photovoltaic power generation scenario;
the constraint indicates that the total electric quantity stored in the energy storage system by the photovoltaic power generation system in the charging station j in the month M and the time t is not more than the electric quantity generated by the photovoltaic system, wherein v mjtk indicates the total electric quantity stored in the energy storage system by the photovoltaic power generation system in the charging station j in the month M and the time t in the kth random scene, p mtk indicates the power generation output power of a unit photovoltaic cell panel in the month M and the t hours in the kth random scene, and M is a month set;
The constraint indicates that the total electric quantity transfer of the photovoltaic power generation from the energy storage system to the automobile battery in the charging station j in month m and time t does not exceed the total requirement of the charging of the motorcade, wherein u mjtk indicates the total electric quantity transfer of the photovoltaic power generation from the energy storage system to the automobile battery in the charging station j in month m and time t in the kth random scene;
The constraint indicates that the current total energy storage amount of the energy storage system does not exceed the energy storage capacity E' j,vmjsk, the total electric quantity stored in the energy storage system by the photovoltaic power generation system in the charging station j in the month m and the time s in the kth random scene, and u mjsk indicates the total electric quantity transfer of the photovoltaic power generation from the energy storage system to the automobile battery in the charging station j in the month m and the time s in the kth random scene;
The constraint indicates that the current total energy storage amount of the energy storage system is more than or equal to 0;
7. the method for locating an optical storage and charging integrated bus charging station according to claim 1, wherein the termination condition is set to a program running time exceeding 10 hours or a number of iterations exceeding 1000.
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